ELM Classifier Research Articles

Vis-NIR and NIR hyperspectral imaging combined with convolutional neural network with attention module for flaxseed varieties identification

The screening and identifying flax germplasm resources are critical for achieving precise flax breeding and variety enhancement. This study integrates hyperspectral imaging (HSI) technology with deep learning to identify flaxseed varieties. Hyperspectral images were captured for 15 flaxseed varieties across two spectral ranges: Vis-NIR (380–1018 nm) and NIR (870–1709 nm). PCA and LDA were utilized to visually cluster these varieties. To automatically learn the spectral features and improve model performance, a one-dimensional convolutional neural network (CAM-TM-1DCNN) embedded with a channel attention module (CAM) and transformer module (TM) was developed for rapid recognition of flaxseed varieties. Experimental results validate the model's efficacy. Compared with ELM, BPNN, LSTM and 1DCNN classification models, the CAM-TM-1DCNN demonstrated superior classification performance in the NIR spectral range, achieving a test accuracy of 95.26 %. Moreover, all models performed better in the NIR spectral range compared to the Vis-NIR spectral range. The study also evaluated the impact of SPA and CARS feature selection algorithms on the classification models, confirming that the full-spectrum-based CAM-TM-1DCNN model outperformed others. These findings suggest that the CAM-TM-1DCNN model can effectively identify flaxseed varieties, providing a novel strategy and viable technical approach for future flaxseed variety recognition based on HSI technology.

Classification method of seabed sonar image substrate based on ELM-AdaBoost

The results of sediment classification are affected by the measurement environment. The water depth has a significant impact on the sound propagation, and the acoustic characteristics will be different under different water depths. In addition, seabed topography, sediment types and their distribution, acoustic frequency, equipment types and settings, and suspended substances in water will all affect the original sonar data. In order to obtain accurate classification results, a seabed sonar image classification method based on Elm AdaBoost is proposed. The original sound intensity data is compensated for gain and the anomaly is eliminated. After preprocessing the multi beam sonar data, the image texture feature is extracted from the gray level co-occurrence matrix as the feature vector for classification, and the elm classifier is constructed. The elm is enhanced by AdaBoost. The calculated feature is input into the elm AdaBoost network for sediment classification, and the result is output. Simulation results show that the proposed method effectively improves the accuracy of sediment classification, and verifies the feasibility of this method.

Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification.

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%.

Pattern recognition of control charts based on data feature enhancement and ensemble learning of classifiers for dimensional accuracy of products

Product quality in manufacturing is directly related to the reliability, durability and safety of the product. Product quality can be controlled by building a product control chart pattern recognition model. The complexity and temporal sequence of product quality data make it difficult to extract effective control chart features, and it is difficult for common classifiers to play an effective role for each control chart pattern category, thus affecting the accuracy of pattern recognition. Therefore, this paper proposes a data feature enhancement processing method that combines Smote algorithm and smooth shift processing. This method reveals data features by performing feature enhancement processing on raw data and extracts statistical and shape features for dimensionality reduction to improve recognition accuracy and efficiency. Based on this approach, this paper also proposes an ensemble learning model based on the Stacking method, which incorporates BP, ELM, PNN and SVM classifiers for control chart pattern recognition. Through extensive experimental validation, the data feature enhancement method improves the accuracy of the weak classifier normal pattern recognition by 30%, and effectively reduces the misclassification in the quality control process. The ensemble learning model is stronger than the weak classifier, and its recognition accuracy can reach 97.31%.

SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification

A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicate Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier.

Development Path of Teaching Method Innovation in Art Colleges and Universities under the Background of Internet

Abstract The informatization teaching method is a big challenge to the traditional form of teaching in art college education. This paper studies the construction of an informatization teaching method combining multimedia technology and multimodal media information retrieval technology. In view of the demand and development conception of teaching methods in art colleges and universities under the background of the Internet, it examines the willingness of teachers of art colleges and universities to teach informatization as well as the degree of satisfaction of their teaching ability. It optimizes the retrieval of learning resources based on the multi-category classification method of ELM classifiers and proposes an integrated mode of informatization teaching in art disciplines. According to the factors affecting the informatization teaching ability of art teachers, teaching design and teacher implementation are carried out in a case-by-case manner to analyze and summarize the program effects of the informatization teaching approach. The correlation coefficient between teachers’ willingness to adopt informative teaching methods and their satisfaction with their information teaching ability is 0.614, which is moderately correlated. After the implementation of the teaching program, the total score of the teacher’s informatization teaching ability reaches 80.052, which improves the level of informatization teaching compared to the level of informatization in pre-preparation. The informatization teaching method’s program design can enhance the teacher’s informatization teaching level, which can have an impact on the innovative development of the teaching method in art colleges and universities through practical experience.

Classification of ischemic and hemorrhagic stroke using Enhanced-CNN deep learning technique

Stroke is a type of cerebrovascular disorder that has a significant impact on people’s lives and well-being. Quantitative investigation of MRI imaging of the brain plays a critical role in analyzing and identifying therapy for stroke. A block primarily provokes stroke in the brain’s blood supply. Deep learning algorithms can be used to identify strokes in patients in a short period. Proposed deep learning methods are used to classify strokes using magnetic resonance imaging (MRI) images. Early detection enhances treatment opportunities and saves lives, which is the primary motivation of the proposed work. Deep learning methods have emerged as significant research trends in recent years, particularly for classifying different types of stroke such as ischemic and hemorrhagic stroke. A dataset of 13,850 MRI images of stroke patients was collected from various reliable sources, including Madras scans and labs, Radiopaedia, Kaggle datasets, and online databases. Among these images, 7,810 were identified as cases of ischemic stroke, while 6,040 represented hemorrhagic strokes. For training purposes, a total of 9,700 images were used, with 4,150 images employed for testing. A comparative analysis of ANN, SVM, NB, ELM, KNN and Enhanced CNN technique is carried out, and 98.4% of classification accuracy is obtained by using Enhanced CNN. Statistical analysis of parameters such as accuracy, precision, F1-score, and recall was conducted, demonstrating that the Enhanced CNN method outperformed SVM, NB,ELM, KNN and ANN classifiers. The Enhanced CNN method achieved an accuracy of 0.984, precision of 0.949, recall of 0.972, and an F1-score of 0.960 on the training dataset, which is significantly higher than the other classifiers. Furthermore, the Enhanced CNN algorithm’s ability to automatically learn features and efficiently process large datasets enhances its potential as a powerful tool for accurately classifying stroke lesions.

An Enhanced CNN-based ELM Classification for Disease Prediction in the Rice Crop

To meet the demands of a constantly expanding population, intensive farming is becoming more popular in the modern day. This strategy, meanwhile, increases the possibility of a wider range of plant illnesses. By reducing crop productivity in terms of both quantity and quality, these infections represent a threat to food production and ultimately result in a fall in the economy. Fortunately, new opportunities for early diagnosis of such epidemics have emerged because of technological improvements, which are advantageous for society as a whole. The difficulties created by technology and bio-mutations create a potential for additional breakthroughs, notwithstanding the significant contributions made by researchers in the field of agricultural disease diagnosis. The suggested framework comprises three key phases: preprocessing, feature extraction, and the classification of leaf diseases. To optimize computational resources and memory utilization, the input image undergoes pre-processing as a preliminary step. Afterward, a Convolutional Neural Network (CNN) is utilized on an extensive dataset of labeled images to capture pertinent features for the diagnosis of rice leaf diseases. The suggested model utilizes an Efficient Selective Pruning of Hidden Nodes (ELM) classifier based on the RBF kernel to classify the input data.

Remote sensing image segmentation using feature based fusion on FCM clustering algorithm

AbstractImage segmentation of heterogeneous comparable objects lying beneath the earth’s surface is a fundamental but challenging research area in remote sensing. Learning approaches are used in remote sensing image segmentation to improve segmentation accuracy at the expense of time and a large amount of data, but their performance need to be finely classified due to information diversity constraints. In this work, we proposed an novel feature based fuzzy C-means-extreme learning machine (FBFCM-ELM) algorithm for remote sensing image segmentation in which the classification based on entropy, intensity, and edge features is performed in such a way that it updates the intensity value to preserve the most local characteristics in the image while still being able to clearly distinguish the image’s boundaries by assigning the pixel values of each cluster to the peak value of the cluster’s sub-histogram. Using FBFCM, features are extracted and used as reliable samples for ELM training. Undetermined segmented pixels are obtained using the trained ELM classifier. Experiments performed over number of images that confirmed the proposed method yields a better segmented RGB image, as evidenced by observable details, edges, and improved appearance that resembles the ground truth image and outperforms state-of-the-art algorithms.

Online intrusion detection mechanism based on model migration in intelligent pumped storage power stations

With the continuous integration of new energy into the power grid, various new attacks continue to emerge and the feature distributions are constantly changing during the deployment of intelligent pumped storage power stations. The intrusion detection model trained on the old data is hard to effectively identify new attacks, and it is difficult to update the intrusion detection model in time when lacking data. To solve this issue, by using model-based transfer learning methods, in this paper we propose a convolutional neural network (CNN) based transfer online sequential extreme learning machine (TOS-ELM) scheme to enable the online intrusion detection, which is called CNN-TOSELM in this paper. In our proposed scheme, we use pre-trained CNN to extract the characteristics of the target domain data as input, and then build online learning classifier TOS-ELM to transfer the parameter of the ELM classifier of the source domain. Experimental results show the proposed CNNTOSELM scheme can achieve better detection performance and extremely short model update time for intelligent pumped storage power stations.

Intelligent classification model of land resource use using deep learning in remote sensing images

Accurate and effective remote sensing image classification and analysis methods can provide reliable support for the sustainable development of land resources, but the current methods can't support the precise processing of complex images. To solve this problem, this paper proposes a new classification and analysis method of remote sensing image land resources based on deep learning. Firstly, this method first uses ENVI software to realize image preprocessing on the collected data set, and realizes reliable data set; At the same time, based on the sparse auto-encoder network model, the sample features of the image data set are deeply mined to make a better reference for category determination; And based on ELM classifier network, convolution layer and sub sampling layer are alternately connected to extract the depth feature of remote sensing image, which can greatly reduce the number of network parameters and improve the computational. Finally, the actual collected data set is used for simulation verification, and the experimental results show that the accuracy of complex image analysis reaches 98.15%, which shows excellent performance of image feature classification and analysis.

A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

Dalgacık Aşırı Öğrenme Makinesi Otomatik Kodlayıcılarına Dayalı Öğretmenlerde EEG, EMG ve EKG Tabanlı Psikososyal Risk Düzeylerinin Belirlenmesi

Teachers who perform a sacred work are faced with many psychosocial risks. These risks can often be caused by the school administration, the students, and environmental factors. Machine learning and data mining approaches have recently gained much attention in social and educational researches. In this study, a novel approach, which is based on data augmentation and data classification, is proposed for the prediction of the psychosocial risk levels of the teachers. The data augmentation is carried out by using an extreme learning machine autoencoders (ELM-AE). More specifically, the wavelet activation function is incorporated into the ELM-AE to develop a novel approach called WELM-AE. After data augmentation, a traditional ELM classifier is used in the prediction of the psychosocial risk levels of teachers. A dataset, which contains physiological factors, namely Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG), is used to evaluate the performance of the proposed method. Classification accuracy is used as the evaluation metric. All coding is carried out in MATLAB, and a 99.9% accuracy score is obtained with the proposed method. A performance comparison is also carried out with some machine learning techniques, namely decision trees (DT), support vector machines (SVM), and K-nearest neighbour (KNN). The results show that the proposed WELM-AE and ELM classifier outperform the compared methods.

Computer aided diagnostic support system for skin cancer using ELM classifier

Computer aided diagnostic support system for skin cancer using ELM classifier

A new collaborative fault identification strategy using multivariate hierarchical dispersion entropy

This article presents a fault recognition strategy using multivariate hierarchical dispersion entropy to monitor the conditions of rolling bearing. First, the vibration data would be measured from multi-channel sensors synchronously. Then, the proposed mvHDE is employed to capture fault information from the collected data. Finally, the fault features are input into the ELM classifier to automatically identify fault types of bearing. The feasibility and effectiveness of the presented intelligent fault diagnosis schemes are verified through experimental studies.

An Integration of Archerfish Hunter Spotted Hyena Optimization and Improved ELM Classifier for Multicollinear Big Data Classification Tasks

An Integration of Archerfish Hunter Spotted Hyena Optimization and Improved ELM Classifier for Multicollinear Big Data Classification Tasks

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

AbstractMulti‐class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi‐channel EEG activity still remains a challenging task. In this study, to enhance the multi‐class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed. First, multi‐channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA‐SOGWO based EEG channel selection criterion. After that, the discriminable feature‐set is generated from time, frequency, and FAWT based time‐frequency domain of EEG dataset. Then, an informative feature‐set is constructed from extracted features through CCA‐RFE based feature selection criterion. Finally, training and validation of selected feature‐set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi‐class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the proposed methodology as an efficient method for future BCI system design.

Extreme wave height detection based on the meteorological data, using hybrid NOF-ELM method

ABSTRACT The realization of accurate extreme wave height occurrence prediction is essential for offshore and onshore structures. In this paper, a new hybrid Natural Outlier Factor-Extreme Learning Machine (NOF-ELM) method is proposed to predict the extreme wave height occurrence based on the meteorological data. Four major hurricanes in the Gulf of Mexico are used to evaluate the performance of the proposed model. Moreover, the effect of metrological parameters on the occurrence of extreme wave height is investigated. The results of the ELM classifier are then compared with traditional classification techniques, including Logistic Regression, C4.5 Decision Trees, Discriminant Analysis, k-Nearest Neighbours, classic Multi-Layer Perceptron neural networks, and Support Vector Machines. The results show that the proposed method performs well in extreme wave height detection based on metrological parameters by mean accuracy higher than 99%. Furthermore, the results indicate that radial basis ELM has the best performance in extreme wave detection.

Research on rolling bearing fault diagnosis method based on ARMA and optimized MOMEDA

In actual operating conditions, rolling bearings vibration signals are easily covered by heavy noise, increasing the difficulty of fault diagnosis. A fault diagnosis method based on auto regressive moving average (ARMA) model and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) algorithm is proposed to address this issue. Firstly, ARMA model is used to remove the intrinsic components and pre-whitening the signals. Then parameters of MOMEDA are optimized by Sparrow Search Algorithm (SSA), the periodic fault signals are recovered by the optimized MOMEDA and the secondary noise reduction of the signals is realized. Finally, a class of time-domain average dimensionless features, namely average pulse factor, average kurtosis factor and average margin factor, are proposed and combined with the Gini index as fault diagnosis indexes then input into ELM classifier to identify fault types. Experimental results show the proposed method can identify fault types effectively and achieve accurate diagnosis of rolling bearings.

Deep Convolutional Neural Network Based Extreme Learning Machine Image Classification

In this paper, we introduce a new deep convolutional neural network based extreme learning machine model for the classification task in order to improve the network's performance. The proposed model has two stages: first, the input images are fed into a convolutional neural network layer to extract deep-learned attributes, and then the input is classified using an ELM classifier. The proposed model achieves good recognition accuracy while reducing computational time on both the MNIST and CIFAR-10 benchmark datasets.